The decreasing cost and increasing performance of embedded smart camera systems makes it attractive to consider applying them to a variety of surveillance and tracking applications. In the near future it may be possible to deploy small, unobtrusive smart cameras in the same way that one deploys light bulbs, providing ubiquitous coverage of extended areas. It would be therefore desirable to have a system and method for using such a system to track passengers at an airport from the time that they arrive at curbside check in to the time that they board their flight. Similarly, it would be desirable to have a system and method to monitor the movements elderly or infirm individuals in their homes in order to improve their quality of care.
There is a need to reliably detect, localize and track targets as they move over an extended area of regard covered by multiple distributed smart cameras. However, there is presently a lack of systems and method that allow detection and tracking which can be distributed over multiple sensors without requiring excessive amounts of communication. These systems would ideally be scalable to allow for deployments that may involve thousands of cameras distributed over extended regions and be robust to failure so that the overall system responds gracefully when individual sensors are added or removed asynchronously.
Most detection and tracking systems that have been developed or proposed fuse information from multiple sensors at a central point in the network which is responsible for establishing tracks and associating measurements from different views. As the number of sensors grows, increasing demands are placed on the communication system which must route information to these processing centers. Moreover, failures in these processing centers can often render the entire network useless.
There have been some approaches to tracking using camera networks. For example Kayumbi, Anjum and Cavallaro describe a scheme for localizing soccer players with a network of distributed cameras. (G. Kayumbi, N. Anjum, and A. Cavallaro, “Global trajectory reconstruction from distributed visual sensors,” in International Conference on Distributed Smart Cameras 08, 2008, pp. 1-8.)
Quinn et. al. propose a scheme for calibrating a set of cameras in a room and using them to track targets. (M. Quinn, R. Mudumbai, T. Kuo, Z. Ni, C. De Leo, and B. S. Manjunath, “Visnet: A distributed vision testbed,” in ACM/IEEE International Conference on Distributed Smart Cameras, 2008, September 2008, pp. 364-371.) Their approach splits the tracking task between a collection of smart camera nodes and a higher level process which fuses the sightings from these cameras. However, there is still a need to develop protocols that can be employed on large networks covering extended areas.
Medeiros, Park and Kak [9] describes a distributed approach to triangulating targets and distributing the tracking task over multiple nodes. (H. Medeiros, J. Park, and A. C. Kak, “Distributed object tracking using a cluster-based kalman filter in wireless camera networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 4, pp. 448^63, August 2008.) This protocol involves electing a leader associated with every tracked object which is responsible for maintaining that track. Klausnet Tengg and Rinner describe a distributed multilevel approach to fusing the measurements gleaned from a network of smart cameras. (Klausner A. Tengg A. Rinner B., “Distributed multilevel data fusion for networked embedded systems,” in Selected Topics in Signal Processing, IEEE Journal of Publication Date: August 2008, August 2008.) There remains a need, as networks grow to allow the protocol to be scalable, such as allowing the cameras to be viewed as peers.